Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images

The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics.

Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways

Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches.

Towards Explainable Dialogue System using Two-Stage Response Generation

In recent years, neural networks have achieved impressive performance on dialogue response generation. However, most of these models still suffer from some shortcomings, such as yielding uninformative responses and lacking explainable ability. The paper proposes a Two-Stage Dialogue Response Generation model (TSRG), which specifies a method to generate diverse and informative responses based on an interpretable procedure between stages. TSRG involves a two-stage framework that generates a candidate response first and then instantiates it as the final response. The positional information and a resident token are injected into the candidate response to stabilize the multi-stage framework, alleviating the shortcomings in the multi-stage framework. Additionally, TSRG allows adjusting and interpreting the interaction pattern between the two generation stages, making the generation response somewhat explainable and controllable. We evaluate the proposed model on three dialogue datasets that contain millions of single-turn message-response pairs between web users. The results show that, compared with the previous multi-stage dialogue generation models, TSRG can produce more diverse and informative responses and maintain fluency and relevance.

Chaotic Sparrow Search Algorithm with Deep Transfer Learning Enabled Breast Cancer Classification on Histopathological Images

Simple Summary

Cancer is considered the most significant public health issue which severely threatens people’s health. The occurrence and mortality rate of breast cancer have been growing consistently. Initial precise diagnostics act as primary factors in improving the endurance rate of patients. Even though there are several means to identify breast cancer, histopathological diagnosis is now considered the gold standard in the diagnosis of cancer. However, the difficulty of histopathological image and the rapid rise in workload render this process time-consuming, and the outcomes might be subjected to pathologists’ subjectivity. Hence, the development of a precise and automatic histopathological image analysis method is essential for the field. Recently, the deep learning method for breast cancer pathological image classification has made significant progress, which has become mainstream in this field. Therefore, in this work, we focused on the design of metaheuristics with deep learning based breast cancer classification process. The proposed model is found to be an effective tool to assist physicians in the decision making process.

Abstract

Breast cancer is the major cause behind the death of women worldwide and is responsible for several deaths each year. Even though there are several means to identify breast cancer, histopathological diagnosis is now considered the gold standard in the diagnosis of cancer. However, the difficulty of histopathological image and the rapid rise in workload render this process time-consuming, and the outcomes might be subjected to pathologists’ subjectivity. Hence, the development of a precise and automatic histopathological image analysis method is essential for the field. Recently, the deep learning method for breast cancer pathological image classification has made significant progress, which has become mainstream in this field. This study introduces a novel chaotic sparrow search algorithm with a deep transfer learning-enabled breast cancer classification (CSSADTL-BCC) model on histopathological images. The presented CSSADTL-BCC model mainly focused on the recognition and classification of breast cancer. To accomplish this, the CSSADTL-BCC model primarily applies the Gaussian filtering (GF) approach to eradicate the occurrence of noise. In addition, a MixNet-based feature extraction model is employed to generate a useful set of feature vectors. Moreover, a stacked gated recurrent unit (SGRU) classification approach is exploited to allot class labels. Furthermore, CSSA is applied to optimally modify the hyperparameters involved in the SGRU model. None of the earlier works have utilized the hyperparameter-tuned SGRU model for breast cancer classification on HIs. The design of the CSSA for optimal hyperparameter tuning of the SGRU model demonstrates the novelty of the work. The performance validation of the CSSADTL-BCC model is tested by a benchmark dataset, and the results reported the superior execution of the CSSADTL-BCC model over recent state-of-the-art approaches.

COVID-19 lockdown impacts on heavy metals and microbes in shallow groundwater and expected health risks in an industrial city of South India

In this investigation, the positive impact of COVID-19 lockdown on heavy metals concentration and biological parameters in the shallow groundwater samples of Coimbatore city of South India was ascertained. The groundwater samples (n=15) were obtained from shallow open wells during before lockdown (24-25 February 2020) and after lockdown (2-3 June 2020) periods. These samples were analysed for heavy metals (Fe, Mn, Ni, Cr and Pb) and biological parameters (E. coli, Fecal coliforms, Fecal streptococci and Total coliforms). Fe concentration was within the permissible limit but, the concentrations of Mn, Ni, Cr and Pb were above the allowable limits for drinking uses as per the WHO. However, after lockdown the number of samples crossing the cutoff limit had considerably decreased (Mn: from 2 to 0 mg/L; Ni: from 13 to 10 mg/L; Cr: 7 to 5 mg/L and Pb: from 13 to 8 mg/L). The heavy metal pollution index (HPI) revealed that 176.75 km² (67.4%) and 85.35 km² (32.6%) areas fell under unsuitable and very poor categories, respectively, during the pre-lockdown period, whereas 138.23 km² (52.6%), 118.98 km² (45.3%) and 4.89 km² (2.1%) areas fell under very poor, poor and good categories, respectively, during the post-lockdown period. Similarly, Total coliform, Fecal coliform and E. coli had decreased distinctly due to the pandemic lockdown. Therefore, the shutdown of small and large-scale industries during the lockdown period had improved the groundwater quality. The health risk assessment showed that 93%, 87% and 80% of pre-lockdown samples, and 87%, 80% and 73% of post-lockdown samples possessed non-carcinogenic risks (HI > 1) for children, female and male categories, respectively.

Internet of Things Enabled Financial Crisis Prediction in Enterprises Using Optimal Feature Subset Selection-Based Classification Model

At present time, an effective tool becomes essential to forecast business failure as well as financial crisis on small- to medium-sized enterprises. This article presents a new optimal feature selection (FS)-based classification model for financial crisis prediction (FCP). The proposed FCP method involves data acquisition, preprocessing, FS, and classification. Initially, the financial data of the enterprises are collected by the use of the internet of things devices, such as smartphones and laptops. Then, the pigeon-inspired optimization (PIO)-based FS technique is applied to choose an optimal set of features. Afterward, the extreme gradient boosting (XGB)-based classification optimized by the Jaya optimization (JO) algorithm called JO-XGB is employed to classify the financial data. The application of the JO algorithm helps to tune the parameters of the XGB model. A detailed experimental validation process takes place to ensure the performance of the presented PIO-JO-XGBoost model. The obtained simulation results verified the effectiveness of the presented model over the compared methods.

An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images

In recent times, COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, pre-processing is applied to enhance the quality of the image. Next, the CRNN model is used for feature extraction, followed by hyperparameter tuning of CRNN via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using the BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model, and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods.

Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network

At present times, financial decisions are mainly based on the classifier technique, which is utilized to allocate a collection of observations into fixed groups. A diverse set of data classifier approaches were presented for forecasting the financial crisis of an institution using the past data. An essential process toward the design of a precise financial crisis prediction (FCP) approach comprises the choice of proper variables (features) that are related to the issues at hand. This is termed as a feature selection (FS) issue that assists to improvise the classifier results. Besides, computational intelligence techniques can be used as a classification model to determine the financial crisis of an organization. In this view, this article introduces a new FS using elephant herd optimization (EHO) with modified water wave optimization (MWWO) algorithm-based deep belief network (DBN) for FCP. The EHO algorithm is applied as a feature selector, and MWWO-DBN is utilized for the classification process. The application of the MWWO algorithm helps to tune the parameters of the DBN model, and the choice of optimal feature subset from the EHO algorithm leads to enhanced classification performance. The experimental results of the proposed model are tested against three benchmark data sets, namely AnalcatData, German Credit, and Australian Credit. The obtained simulation results indicated the superior performance of the proposed model by attaining maximum classification performance.

IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19 disease. It is essential to identify and classify the disease through the use of image processing techniques. So, a new intelligent disease diagnosis model is in need to identify the COVID-19. In this view, this paper presents a novel IoT enabled Depthwise separable convolution neural network (DWS-CNN) with Deep support vector machine (DSVM) for COVID-19 diagnosis and classification. The proposed DWS-CNN model aims to detect both binary and multiple classes of COVID-19 by incorporating a set of processes namely data acquisition, Gaussian filtering (GF) based preprocessing, feature extraction, and classification. Initially, patient data will be collected in the data acquisition stage using IoT devices and sent to the cloud server. Besides, the GF technique is applied to remove the existence of noise that exists in the image. Then, the DWS-CNN model is employed for replacing default convolution for automatic feature extraction. Finally, the DSVM model is applied to determine the binary and multiple class labels of COVID-19. The diagnostic outcome of the DWS-CNN model is tested against Chest X-ray (CXR) image dataset and the results are investigated interms of distinct performance measures. The experimental results ensured the superior results of the DWS-CNN model by attaining maximum classification performance with the accuracy of 98.54% and 99.06% on binary and multiclass respectively.

Groundwater Pollution and Human Health Risks in an Industrialized Region of Southern India: Impacts of the COVID-19 Lockdown and the Monsoon Seasonal Cycles

Samples of groundwater were collected during a post-monsoon period (January) and a pre-monsoon period (May) in 2020 from 30 locations in the rapidly developing industrial and residential area of the Coimbatore region in southern India. These sampling periods coincided with times before and during the lockdown in industrial activity and reduced agricultural activity that occurred in the region due to the COVID-19 pandemic. This provided a unique opportunity to evaluate the effects of reduced anthropogenic activity on groundwater quality. Approximately 17% of the wells affected by high fluoride concentrations in the post-monsoon period returned to levels suitable for human consumption in samples collected in the pre-monsoon period. This was probably due to ion exchange processes, infiltration of rainwater during the seasonal monsoon that diluted concentrations of ions including geogenic fluoride, as well as a reduction in anthropogenic inputs during the lockdown. The total hazard index for fluoride in the post-monsoon samples calculated for children, adult women, and adult men indicated that 73%, 60%, and 50% of the groundwater samples, respectively, had fluoride levels higher than the permissible limit. In this study, nitrate pollution declined by 33.4% by the pre-monsoon period relative to the post-monsoon period. The chemical facies of groundwater reverted from the Na-HCO3-Cl and Na-Cl to the Ca-HCO3 type in pre-monsoon samples. Various geogenic indicators like molar ratios, inter-ionic relations along with graphical tools demonstrated that plagioclase mineral weathering, carbonate dissolution, reverse ion exchange, and anthropogenic inputs are influencing the groundwater chemistry of this region. These findings were further supported by the saturation index assessed for the post- and pre-monsoon samples. COVID-19 lockdown considerably reduced groundwater pollution by Na+, K+, Cl-, NO3¯, and F- ions due to shutdown of industries and reduced agricultural activities. Further groundwater quality improvement during lockdown period there is evidence that the COVID-19 lockdown by increased HCO3¯ ion concentration. Overall results illustrate the positive benefits to groundwater quality that could occur as a result of measures to control anthropogenic inputs of pollutants.

Comparison of peritubal infiltration and single level T10 paravertebral block in percutaneous nephrolithotomy (PCNL)

Background and Aims:

Material and Methods:

Results:

Conclusion:

A Cross-sectional Study on Cutaneous Side- effects Associated with Mask Usage among Doctors during COVID-19 Pandemic

Introduction: The Coronavirus Disease-2019 (COVID-19) pandemic has brought major changes in people’s lifestyle, especially in healthcare workers. Healthcare workers caring for COVID-19 patients are spending long hours wearing Personal Protective Equipment (PPE). There are reports of adverse skin reactions secondary to wearing PPE, especially face masks. However, it is essential to wear the protective equipment. Aim: To assess the proportion of doctors who report adverse skin reaction after the use of face masks and enlist the skin reactions reported. Also to study the relationship between certain suspected factors and occurrence of skin reactions. Materials and Methods: This was a cross-sectional study conducted using a questionnaire containing both open and closed ended questions which was distributed through online platform. The questionnaire contained details on the type of mask, duration of usage, frequency of change and dermatological manifestations experienced. Sample population constituted doctors who were willing to participate in the study. IBM Statistical Package for the Social Sciences (SPSS) version 17.0 was used for analysis of data. Results: The male to female ratio among the 220 doctors studied was 1:1.59. Maximum number of doctors (56.4%) wore N95 masks. Total 33.63% of them used one new mask every day and 60.90% of them used the mask for more than 6 hours continuously on a day. Acne was the most commonly reported problem accounting for 48.2%. Doctors using N95 masks reported acne more commonly. Conclusion: N95 masks were the most commonly used masks. Total 88.18% of the doctors reported cutaneous manifestations. Acne was the most common dermatological problem reported. It was associated with the use of N95 masks and longer duration of use of N95 masks.

Suitability of groundwater quality for irrigation in and around the main Gadilam river basin on the east coast of southern India

The main intent of this study was to investigate the condition of groundwater quality for irrigation purposes in and around the main Gadilam river basin, the east coast of southern India. A total of fifty groundwater samples were collected and analyzed for various parameters such as electrical conductivity (EC), pH, TDS, major cations (Ca2+, Mg2+, Na+, and K+) and anions (SO42-, Cl-, HCO3-, and NO3-). Irrigation water quality parameters like the sodium absorption ratio (SAR), residual sodium carbonate (RSC), percentage sodium (%Na), magnesium hazard (MH), permeability index (PI), and Kelly ratio (KR) were computed to assess the irrigation water quality of groundwater. Furthermore, graphical representation diagrams such as USSL, Wilcox, and Doneen have been prepared for irrigation water quality. From the computation of SAR, Na%, RSC, PI, and KR values, it was found that 100% of groundwater samples were found to be suitable for irrigation purposes. Besides, USSL and Doneen diagrams show that the samples are safe for irrigation usage. The Wilcox diagram in the classification of electrical conductivity reveals that most samples fall into the good to permissible class (78%), in doubtful to unsuitable class (20%), and 2% of samples are unsuitable. Magnesium hazards of 82% of the groundwater samples are suitable for irrigation, while the remaining 18% of the samples exceeded the limit and found to be unsuitable for irrigation purposes. The study concludes that higher percentages of groundwater samples were suitable for irrigation purposes in the study area, and the concentration of magnesium influenced groundwater at a few locations.

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits. So, the design and implementation of COVID-19 testing kits remain an open research problem. Several findings attained using radio-imaging approaches recommend that the images comprise important data related to coronaviruses. The application of recently developed artificial intelligence (AI) techniques, integrated with radiological imaging, is helpful in the precise diagnosis and classification of the disease. In this view, the current research paper presents a novel fusion model hand-crafted with deep learning features called FM-HCF-DLF model for diagnosis and classification of COVID-19. The proposed FM-HCF-DLF model comprises three major processes, namely Gaussian filtering-based preprocessing, FM for feature extraction and classification. FM model incorporates the fusion of handcrafted features with the help of local binary patterns (LBP) and deep learning (DL) features and it also utilizes convolutional neural network (CNN)-based Inception v3 technique. To further improve the performance of Inception v3 model, the learning rate scheduler using Adam optimizer is applied. At last, multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model was experimentally validated using chest X-ray dataset. The experimental outcomes inferred that the proposed model yielded superior performance with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%.

Dataset on the suitability of groundwater for drinking and irrigation purposes in the Sarabanga River region, Tamil Nadu, India

The present datasets reveal that to assess the suitability of groundwater quality for drinking and irrigation uses in both Pre and Post Monsoon Season in Sarabanga River region, Tamilnadu, India based on various water quality indices. A total of 50 groundwater samples were collected in different location in a research area. Water Quality Index (WQI) is a number which indicates the suitability of water for drinking purpose. Sodium Absorption Ratio (SAR), Permeability Index (PI), Residual Sodium Carbonate (RSC), Percentage Sodium (%Na), Kelly Ratio (KR) and Magnesium Hazards (MH) are index value which elaborates the fitness of groundwater for agriculture uses. The WQI value for groundwater in both seasons reveals that 74.5 sq.km and 37.24 sq.km of the area were unfit for domestic purposes. Based on irrigation indices, almost all sample locations are suitable for irrigation purposes. The dataset demonstrates how water quality indices would be applied to policymakers to manage, handle and sustainably improve society at large.

Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window, split window algorithm and spectral radiance model

In the past, decadal time-series analysis has been done traditionally using meteorological data. In particular, decadal analysis of land surface temperature has been a major issue due to the unavailability of remote sensing techniques. But, nowadays, with the recent advances in remote sensing techniques and modern software Land Surface Temperature (LST) can be calculated through the thermal bands. LST can be estimated through many algorithms such as Split-window, Mono-Window (SW), Single-Channel (SH), among others. LST was estimated using Mono-Window algorithm on Landsat-5 TM, Landsat-7 ETM+ and split window algorithm on Landsat-8 OLI/TIRS Thermal Infrared (TIR) bands. Vegetation index was obtained by using Normalized Difference Vegetation Index (NDVI) from red and Near-Infrared (NIR) bands. NDVI has been effectively used in vegetation monitoring and to analyze the vegetation in responses to climate change such as surface temperature variation. The twelve Weredas (third-level administrative divisions) of Ethiopia which are highly prone to drought were selected to investigate decadal land surface temperature variations and its impact on the surrounding environment, especially on vegetation cover. Ten Landsat images of three different sensors from 1999 to 2018 were used as the basic data source. The processed data of surface temperature and vegetation indices showed a strong correlation. The higher LST values indicate the smaller NDVI and vice versa and it is also identified the areas with high temperature being barren regions and areas with low temperature covered with more vegetation.

Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.

Data on land use and land cover changes in Adama Wereda, Ethiopia, on ETM+, TM and OLI- TIRS landsat sensor using PCC and CDM techniques

Land use and land cover changes are often referred for the anthropogenic modification of Earth's surface. The extents of land use and land cover (LULC) changes in Adama Wereda at three different periods (2002, 2010, and 2017) were generated using data from various Landsat sensors namely ETM+, TM and OLI TIRS. This work focused on a change detection analysis using post classification comparison (PCC) and change detection matrix (CDM). These images were geometrically corrected and image processing operations for instance: radiometric correction, using spectral radiance model was carried out, followed by land cover categorisation into water bodies, built up, bare land, sparse vegetation and dense vegetation employing Knowledge, pixel and indices based classification in ERDAS imagine software. The generated data of both change detection techniques from 2002 to 2017 revealed interesting aspect that build up, dense vegetation and sparse vegetation increased in area of approximately 160%, 30% and 78% respectively at the expense of barren land which decreased at 8.5%, but there is not much change in the water bodies. It was also noticed that both the algorithms gives similar values but with negligible deviation.

Dataset on the assessment of water quality of surface water in Kalingarayan Canal for heavy metal pollution, Tamil Nadu

This data article aimed to investigate the quality of surface water in Kalingarayan Canal for heavy metal pollution, Tamil Nadu. Eight heavy metals like Fe, Cu, Mn, Cr, Zn, Cd, Pb, and Ni were analyzed in the water, for a period of three years, spanning the time frame between January 2014 to December 2016. Eight stations were selected along the Kalingarayan Canal, and water samples were collected on a monthly basis from these stations. The pH of the samples was in the alkaline state (6.88–8.90), whereas conductance was in the range of 394–4276 µs/cm. The average concentration of heavy metals in the surface water ranges from 0.040 to 10.75, 0.030 to 0.890, 0.02 to 0.91, 0.00 to 1.96, 0.00 to 0.01, 0.00 to 0.053, 0.01 to 0.12 and 0.110 to 3.40 mg/L for the metals Fe, Mn, Zn, Cu, Cd, Ni, Pb and Cr respectively. The dominance of various heavy metals in the surface water follows the sequence: Fe > Cr > Cu > Zn > Mn > Pb > Ni > Cd respectively. The canal is affected by anthropogenic activities and industrialization in terms of heavy metals.

High-fat, high-fructose, high-cholesterol feeding causes severe NASH and cecal microbiota dysbiosis in juvenile Ossabaw swine

Pediatric obesity and nonalcoholic steatohepatitis (NASH) are on the rise in industrialized countries, yet our ability to mechanistically examine this relationship is limited by the lack of a suitable higher animal models. Here, we examined the effects of high-fat, high-fructose corn syrup, high-cholesterol Western-style diet (WD)-induced obesity on NASH and cecal microbiota dysbiosis in juvenile Ossabaw swine. Juvenile female Ossabaw swine (5 wk old) were fed WD (43.0% fat; 17.8% high-fructose corn syrup; 2% cholesterol) or low-fat diet (CON/lean; 10.5% fat) for 16 wk ( n = 6 each) or 36 wk ( n = 4 each). WD-fed pigs developed obesity, dyslipidemia, and systemic insulin resistance compared with CON pigs. In addition, obese WD-fed pigs developed severe NASH, with hepatic steatosis, hepatocyte ballooning, inflammatory cell infiltration, and fibrosis after 16 wk, with further exacerbation of histological inflammation and fibrosis after 36 wk of WD feeding. WD feeding also resulted in robust cecal microbiota changes including increased relative abundances of families and genera in Proteobacteria ( P < 0.05) (i.e., Enterobacteriaceae, Succinivibrionaceae, and Succinivibrio) and LPS-containing Desulfovibrionaceae and Desulfovibrio and a greater ( P < 0.05) predicted microbial metabolic function for LPS biosynthesis, LPS biosynthesis proteins, and peptidoglycan synthesis compared with CON-fed pigs. Overall, juvenile Ossabaw swine fed a high-fat, high-fructose, high-cholesterol diet develop obesity and severe microbiota dysbiosis with a proinflammatory signature and a NASH phenotype directly relevant to the pediatric/adolescent and young adult population.

100-fold improvement in carrier drift mobilities in alkanephosphonate-passivated monocrystalline TiO2 nanowire arrays

Single crystal rutile titania nanowires grown by solvothermal synthesis are actively being researched for use as electron transporting scaffolds in perovskite solar cells, in low detection limit ultraviolet photodetectors, in photoelectrochemical water-splitting, and in chemiresistive and electrochemical sensing. The electron drift mobility (μ _n ) in solution-grown TiO₂ nanowires is very low due to a high density of deep traps, and reduces performance in optoelectronic devices. In this study, the effects of molecular passivation of the nanowire surface by octadecylphosphonic acid (ODPA), on carrier transport in TiO₂ nanowire ensembles, were investigated using transient space charge limited current measurements. Infrared spectroscopy indicated the formation of a highly ordered phosphonate monolayer with a high likelihood of bidentate binding of ODPA to the rutile surface. We report the hole drift mobility (μ _p ) for the first time in unpassivated solvothermal rutile nanowires to be 8.2 × 10^-5 cm² V^-1 s^-1 and the use of ODPA passivation resulted in μ _p improving by nearly two orders of magnitude to 7.1 × 10^-3 cm² V^-1 s^-1. Likewise, ODPA passivation produced between a 2 and 3 order of magnitude improvement in μ _n from ∼10^-5-10^-6 cm² V^-1 s^-1 to ∼10^-3 cm² V^-1 s^-1. The bias dependence of the post-transit photocurrent decays in ODPA-passivated nanowires indicated that minority carriers were lost to trapping and/or monomolecular recombination for small values of bias (<5 V). Bimolecular recombination was indicated to be the dominant recombination mechanism at higher bias values.

Gut microbiota are linked to increased susceptibility to hepatic steatosis in low-aerobic-capacity rats fed an acute high-fat diet

Poor aerobic fitness is linked to nonalcoholic fatty liver disease and increased all-cause mortality. We previously found that rats with a low capacity for running (LCR) that were fed an acute high-fat diet (HFD; 45% kcal from fat) for 3 days resulted in positive energy balance and increased hepatic steatosis compared with rats that were highly aerobically fit with a high capacity for running (HCR). Here, we tested the hypothesis that poor physiological outcomes in LCR rats following acute HFD feeding are associated with alterations in cecal microbiota. LCR rats exhibited greater body weight, feeding efficiency, 3 days of body weight change, and liver triglycerides after acute HFD feeding compared with HCR rats. Furthermore, compared with HCR rats, LCR rats exhibited reduced expression of intestinal tight junction proteins. Cecal bacterial 16S rDNA revealed that LCR rats had reduced cecal Proteobacteria compared with HCR rats. Microbiota of HCR rats consisted of greater relative abundance of Desulfovibrionaceae and unassigned genera within this family, suggesting increased reduction of endogenous mucins and proteins. Although feeding rats an acute HFD led to reduced Firmicutes in both strains, short-chain fatty acid-producing Phascolarctobacterium was reduced in LCR rats. In addition, Ruminococcae and Ruminococcus were negatively correlated with energy intake in the LCR/HFD rats. Predicted metagenomic function suggested that LCR rats had a greater capacity to metabolize carbohydrate and energy compared with HCR rats. Overall, these data suggest that the populations and metabolic capacity of the microbiota in low-aerobically fit LCR rats may contribute to their susceptibility to acute HFD-induced hepatic steatosis and poor physiologic outcomes.